دورية أكاديمية
A machine learning approach for predicting textbook outcome after cytoreductive surgery and hyperthermic intraperitoneal chemotherapy.
| العنوان: | A machine learning approach for predicting textbook outcome after cytoreductive surgery and hyperthermic intraperitoneal chemotherapy. |
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| المؤلفون: | Ashraf Ganjouei, Amir, Romero-Hernandez, Fernanda, Wang, Jaeyun Jane, Hamed, Ahmed, Alaa, Ahmed, Bartlett, David, Alseidi, Adnan, Choudry, Mohammad Haroon, Adam, Mohamed |
| المصدر: | World J Surg ; ISSN:1432-2323 ; Volume:48 ; Issue:6 |
| بيانات النشر: | Wiley |
| سنة النشر: | 2024 |
| المجموعة: | PubMed Central (PMC) |
| مصطلحات موضوعية: | CRS‐HIPEC, machine learning, textbook outcome |
| الوصف: | Peritoneal carcinomatosis is considered a late-stage manifestation of neoplastic diseases. Cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) can be an effective treatment for these patients. However, the procedure is associated with significant morbidity. Our aim was to develop a machine learning model to predict the probability of achieving textbook outcome (TO) after CRS-HIPEC using only preoperatively known variables. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| العلاقة: | https://doi.org/10.1002/wjs.12184Test; https://pubmed.ncbi.nlm.nih.gov/38651936Test |
| DOI: | 10.1002/wjs.12184 |
| الإتاحة: | https://doi.org/10.1002/wjs.12184Test https://pubmed.ncbi.nlm.nih.gov/38651936Test |
| حقوق: | © 2024 International Society of Surgery/Société Internationale de Chirurgie (ISS/SIC). |
| رقم الانضمام: | edsbas.97EBE2F1 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1002/wjs.12184 |
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